Method and device for determining downlink transmission timing for relay node in next generation communication system

ABSTRACT

The present application discloses a method for transmitting a downlink signal by a child node in a next generation wireless communication system. Specifically, the method comprises the steps of: receiving information on a timing advanced value from a parent node; determining a reception timing of a first downlink signal transmitted from the parent node; calculating a transmission timing of a second downlink signal preceding the reception timing of the first downlink signal by a timing correction value based on a preconfigured offset value and the timing advanced value; and according to the transmission timing of the second downlink signal, transmitting the second downlink signal to another child node.

TECHNICAL FIELD

The present disclosure relates to a wireless communication system and,more particularly, to a method of determining a downlink transmissiontiming for a relay node in a next-generation communication system, andan apparatus therefor.

BACKGROUND

As more and more communication devices demand larger communicationtraffic along with the current trends, a future-generation 5 ^(th)generation (5G) system is required to provide an enhanced wirelessbroadband communication, compared to the legacy LTE system. In thefuture-generation 5G system, communication scenarios are divided intoenhanced mobile broadband (eMBB), ultra-reliability and low-latencycommunication (URLLC), massive machine-type communication (mMTC), and soon.

Herein, eMBB is a future-generation mobile communication scenariocharacterized by high spectral efficiency, high user experienced datarate, and high peak data rate, URLLC is a future-generation mobilecommunication scenario characterized by ultra-high reliability,ultra-low latency, and ultra-high availability (e.g., vehicle toeverything (V2X), emergency service, and remote control), and mMTC is afuture-generation mobile communication scenario characterized by lowcost, low energy, short packet, and massive connectivity (e.g., Internetof things (IoT)).

SUMMARY

Based on the above-described discussion, a method of determining adownlink transmission timing for a relay node in a next-generationcommunication system, and an apparatus therefor will be proposedhereinbelow.

According to an aspect of the present disclosure, provided herein is amethod of transmitting a downlink signal by a child node in anext-generation wireless communication system, including receivinginformation about a timing advance value from a parent node; determininga reception timing of a first downlink signal transmitted by the parentnode; calculating a transmission timing of a second downlink signal byadvancing the transmission timing of the second downlink signal by atiming correction value, based on the timing advance value and a presetoffset value, from the reception timing of the first downlink signal;and transmitting the second downlink signal to another child nodeaccording to the transmission timing of the second downlink signal.

In another aspect of the present disclosure, provided herein is a relaynode in a next-generation wireless communication system, including awireless communication module; at least one processor; and at least onememory connected operably to the at least one processor and configuredto store instructions for causing the at least one processor to performa specific operation based on execution of the instructions, wherein thespecific operation includes receiving information about a timing advancevalue from a parent node, determining a reception timing of a firstdownlink signal transmitted by the parent node, calculating atransmission timing of a second downlink signal by advancing thetransmission timing of the second downlink signal by a timing correctionvalue, based on the timing advance value and a preset offset value, fromthe reception timing of the first downlink signal, and transmitting thesecond downlink signal to another child node according to thetransmission timing of the second downlink signal.

the transmission timing of the first downlink signal and thetransmission timing of the second downlink signal may be identical.

The preset offset value may be included in a random access responsemessage received from the parent node or may be provided by higher layersignaling. The timing advance value may be a most recently updatedtiming advance value from reception of the preset offset value.

The timing correction value may be a sum of a half of the timing advancevalue and the preset offset value.

According to an embodiment of the present disclosure, a relay node in anext-generation communication system may more efficiently determine adownlink transmission timing.

It will be appreciated by persons skilled in the art that the effectsthat could be achieved with the present disclosure are not limited towhat has been particularly described hereinabove and other advantages ofthe present disclosure will be more clearly understood from thefollowing detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating the control-plane and user-planearchitecture of radio interface protocols between a user equipment (UE)and an evolved UMTS terrestrial radio access network (E-UTRAN) inconformance to a 3rd generation partnership project (3GPP) radio accessnetwork standard.

FIG. 2 is a diagram illustrating physical channels and a general signaltransmission method using the physical channels in a 3GPP system.

FIG. 3 illustrates a structure of a radio frame in a Long Term Evolution(LTE) system.

FIGS. 4, 5 and 6 are diagrams illustrating structures of a radio frameand slots used in a new RAT (NR) system.

FIG. 7 abstractly illustrates a hybrid beamforming structure in terms ofTXRUs and physical antennas.

FIG. 8 illustrates a beam sweeping operation for an SS and systeminformation during DL transmission.

FIG. 9 illustrates a cell in a new radio access technology (NR) system.

FIG. 10 is a diagram illustrating a method of determining a DLtransmission timing of an IAB node.

FIG. 11 is a flowchart of a method for performing DL transmissionaccording to an embodiment of the present disclosure.

FIG. 12 is a block diagram illustrating elements of a device forimplementing embodiments of the present disclosure.

FIGS. 13 to 15 are diagrams illustrating an artificial intelligence (AI)system and device for implementing embodiments of the presentdisclosure.

DETAILED DESCRIPTION

The configuration, operation, and other features of the presentdisclosure will readily be understood with embodiments of the presentdisclosure described with reference to the attached drawings.Embodiments of the present disclosure as set forth herein are examplesin which the technical features of the present disclosure are applied toa 3rd generation partnership project (3GPP) system.

While embodiments of the present disclosure are described in the contextof long term evolution (LTE) and LTE-advanced (LTE-A) systems, they arepurely exemplary. Therefore, the embodiments of the present disclosureare applicable to any other communication system as long as the abovedefinitions are valid for the communication system.

The term, base station (BS) may be used to cover the meanings of termsincluding remote radio head (RRH), evolved Node B (eNB or eNode B),transmission point (TP), reception point (RP), relay, and so on.

Artificial Intelligence (AI)

AI refers to the field of studying AI or methodology for making thesame, and machine learning refers to the field of defining variousissues dealt with in the AI field and studying methodology for solvingthe various issues. The machine learning is defined as an algorithm thatenhances the performance of a certain task through consistentexperiences with the task.

An artificial neural network (ANN) is a model used in the machinelearning and may mean a whole model of problem-solving ability which iscomposed of artificial neurons (nodes) that form a network by synapticconnections. The ANN may be defined by a connection pattern betweenneurons in different layers, a learning process for updating modelparameters, and an activation function for generating an output value.

The ANN may include an input layer, an output layer, and optionally oneor more hidden layers. Each layer includes one or more neurons, and theANN may include a synapse that links neurons. In the ANN, each neuronmay output the function value of the activation function for inputsignals, weights, and bias input through the synapse.

The model parameter refers to a parameter determined through learningand includes the weight value of a synaptic connection and the bias of aneuron. A hyperparameter means a parameter to be set in the machinelearning algorithm before learning and includes a learning rate, arepetition number, a mini-batch size, and an initialization function.

The purpose of the learning of the ANN may be to determine the modelparameter that minimizes a loss function. The loss function may be usedas an index to determine the optimal model parameter in the learningprocess of the ANN.

Machine learning may be classified into supervised learning,unsupervised learning, and reinforcement learning according to learningmechanisms.

The supervised learning may refer to a method of training the ANN in astate that labels for learning data are given, and the label may mean acorrect answer (or result value) that the ANN must infer when thelearning data is input to the ANN. The unsupervised learning may referto a method of training the ANN in a state that labels for learning dataare not given. The reinforcement learning may refer to a method oflearning an agent defined in a certain environment to select a behavioror a behavior sequence that maximizes cumulative compensation in eachstate.

Machine learning implemented with a deep neural network (DNN) includinga plurality of hidden layers among ANNs is referred to as deep learning.The deep running is part of the machine running. The machine learningused herein includes the deep running.

Robot

A robot may refer to a machine that automatically processes or operatesa given task based on its own ability. In particular, a robot having afunction of recognizing an environment and making a self-determinationmay be referred to as an intelligent robot.

Robots may be classified into industrial robots, medical robots, homerobots, military robots, etc. according to use purposes or fields.

The robot may include a driving unit having an actuator or a motor andperform various physical operations such as moving a robot joint. Inaddition, a movable robot may include a driving unit having a wheel, abrake, a propeller, etc. and may travel on the ground or fly in the airthrough the driving unit.

Autonomous Driving (Self-Driving)

Autonomous driving refers to a technique of driving by itself. Anautonomous driving vehicle refers to a vehicle moving with no usermanipulation or with minimum user manipulation.

For example, the autonomous driving may include a technology formaintaining a current lane, a technology for automatically adjusting aspeed such as adaptive cruise control, a technique for automaticallymoving along a predetermined route, and a technology for automaticallysetting a route and traveling along the route when a destination isdetermined.

The vehicle may include a vehicle having only an internal combustionengine, a hybrid vehicle having an internal combustion engine and anelectric motor together, and an electric vehicle having only an electricmotor. Further, the vehicle may include not only an automobile but alsoa train, a motorcycle, etc.

The autonomous driving vehicle may be regarded as a robot having theautonomous driving function.

Extended Reality (XR)

Extended reality is collectively referred to as virtual reality (VR),augmented reality (AR), and mixed reality (MR). The VR technologyprovides real-world objects and backgrounds as CG images, the ARtechnology provides virtual CG images on real object images, and the MRtechnology is a computer graphic technology of mixing and combiningvirtual objects with the real world.

The MR technology is similar to the AR technology in that real andvirtual objects are shown together. However, the MR technology isdifferent from the AR technology in that the AR technology uses virtualobjects to complement real objects, whereas the MR technology deal withvirtual and real objects in the same way.

The XR technology may be applied to a HMD, a head-up display (HUD), amobile phone, a tablet PC, a laptop computer, a desktop computer, a TV,a digital signage, etc. A device to which the XR technology is appliedmay be referred to as an XR device.

5G communication involving a new radio access technology (NR) systemwill be described below.

Three key requirement areas of 5G are (1) enhanced mobile broadband(eMBB), (2) massive machine type communication (mMTC), and (3)ultra-reliable and low latency communications (URLLC).

Some use cases may require multiple dimensions for optimization, whileothers may focus only on one key performance indicator (KPI). 5Gsupports such diverse use cases in a flexible and reliable way.

eMBB goes far beyond basic mobile Internet access and covers richinteractive work, media and entertainment applications in the cloud oraugmented reality (AR). Data is one of the key drivers for 5G and in the5G era, we may for the first time see no dedicated voice service. In 5G,voice is expected to be handled as an application program, simply usingdata connectivity provided by a communication system. The main driversfor an increased traffic volume are the increase in the size of contentand the number of applications requiring high data rates. Streamingservices (audio and video), interactive video, and mobile Internetconnectivity will continue to be used more broadly as more devicesconnect to the Internet. Many of these applications require always-onconnectivity to push real time information and notifications to users.Cloud storage and applications are rapidly increasing for mobilecommunication platforms. This is applicable for both work andentertainment. Cloud storage is one particular use case driving thegrowth of uplink data rates. 5G will also be used for remote work in thecloud which, when done with tactile interfaces, requires much lowerend-to-end latencies in order to maintain a good user experience.Entertainment, for example, cloud gaming and video streaming, is anotherkey driver for the increasing need for mobile broadband capacity.Entertainment will be very essential on smart phones and tabletseverywhere, including high mobility environments such as trains, carsand airplanes. Another use case is AR for entertainment and informationsearch, which requires very low latencies and significant instant datavolumes.

One of the most expected 5G use cases is the functionality of activelyconnecting embedded sensors in every field, that is, mMTC. It isexpected that there will be 20.4 billion potential Internet of things(IoT) devices by 2020. In industrial IoT, 5G is one of areas that playkey roles in enabling smart city, asset tracking, smart utility,agriculture, and security infrastructure.

URLLC includes services which will transform industries withultra-reliable/available, low latency links such as remote control ofcritical infrastructure and self-driving vehicles. The level ofreliability and latency are vital to smart-grid control, industrialautomation, robotics, drone control and coordination, and so on.

5G communication involving a new radio access technology (NR) systemwill be described below.

5G may complement fiber-to-the home (FTTH) and cable-based broadband (ordata-over-cable service interface specifications (DOCSIS)) as a means ofproviding streams at data rates of hundreds of megabits per second togiga bits per second. Such a high speed is required for TV broadcasts ator above a resolution of 4K (6K, 8K, and higher) as well as virtualreality (VR) and AR. VR and AR applications mostly include immersivesport games. A special network configuration may be required for aspecific application program. For VR games, for example, game companiesmay have to integrate a core server with an edge network server of anetwork operator in order to minimize latency.

The automotive sector is expected to be a very important new driver for5G, with many use cases for mobile communications for vehicles. Forexample, entertainment for passengers requires simultaneous highcapacity and high mobility mobile broadband, because future users willexpect to continue their good quality connection independent of theirlocation and speed. Other use cases for the automotive sector are ARdashboards. These display overlay information on top of what a driver isseeing through the front window, identifying objects in the dark andtelling the driver about the distances and movements of the objects. Inthe future, wireless modules will enable communication between vehiclesthemselves, information exchange between vehicles and supportinginfrastructure and between vehicles and other connected devices (e.g.,those carried by pedestrians). Safety systems may guide drivers onalternative courses of action to allow them to drive more safely andlower the risks of accidents. The next stage will be remote-controlledor self-driving vehicles. These require very reliable, very fastcommunication between different self-driving vehicles and betweenvehicles and infrastructure. In the future, self-driving vehicles willexecute all driving activities, while drivers are focusing on trafficabnormality elusive to the vehicles themselves. The technicalrequirements for self-driving vehicles call for ultra-low latencies andultra-high reliability, increasing traffic safety to levels humanscannot achieve.

Smart cities and smart homes, often referred to as smart society, willbe embedded with dense wireless sensor networks. Distributed networks ofintelligent sensors will identify conditions for cost- andenergy-efficient maintenance of the city or home. A similar setup can bedone for each home, where temperature sensors, window and heatingcontrollers, burglar alarms, and home appliances are all connectedwirelessly. Many of these sensors are typically characterized by lowdata rate, low power, and low cost, but for example, real time highdefinition (HD) video may be required in some types of devices forsurveillance.

The consumption and distribution of energy, including heat or gas, isbecoming highly decentralized, creating the need for automated controlof a very distributed sensor network. A smart grid interconnects suchsensors, using digital information and communications technology togather and act on information. This information may include informationabout the behaviors of suppliers and consumers, allowing the smart gridto improve the efficiency, reliability, economics and sustainability ofthe production and distribution of fuels such as electricity in anautomated fashion. A smart grid may be seen as another sensor networkwith low delays.

The health sector has many applications that may benefit from mobilecommunications. Communications systems enable telemedicine, whichprovides clinical health care at a distance. It helps eliminate distancebarriers and may improve access to medical services that would often notbe consistently available in distant rural communities. It is also usedto save lives in critical care and emergency situations. Wireless sensornetworks based on mobile communication may provide remote monitoring andsensors for parameters such as heart rate and blood pressure.

Wireless and mobile communications are becoming increasingly importantfor industrial applications. Wires are expensive to install andmaintain, and the possibility of replacing cables with reconfigurablewireless links is a tempting opportunity for many industries. However,achieving this requires that the wireless connection works with asimilar delay, reliability and capacity as cables and that itsmanagement is simplified. Low delays and very low error probabilitiesare new requirements that need to be addressed with 5G

Finally, logistics and freight tracking are important use cases formobile communications that enable the tracking of inventory and packageswherever they are by using location-based information systems. Thelogistics and freight tracking use cases typically require lower datarates but need wide coverage and reliable location information.

The 3GPP communication standards define downlink (DL) physical channelscorresponding to resource elements (REs) carrying information originatedfrom a higher layer, and DL physical signals which are used in thephysical layer and correspond to REs which do not carry informationoriginated from a higher layer. For example, physical downlink sharedchannel (PDSCH), physical broadcast channel (PBCH), physical multicastchannel (PMCH), physical control format indicator channel (PCFICH),physical downlink control channel (PDCCH), and physical hybrid ARQindicator channel (PHICH) are defined as DL physical channels, andreference signals (RSs) and synchronization signals (SSs) are defined asDL physical signals. An RS, also called a pilot signal, is a signal witha predefined special waveform known to both a gNode B (gNB) and a userequipment (UE). For example, cell specific RS, UE-specific RS (UE-RS),positioning RS (PRS), and channel state information RS (CSI-RS) aredefined as DL RSs. The 3GPP LTE/LTE-A standards define uplink (UL)physical channels corresponding to REs carrying information originatedfrom a higher layer, and UL physical signals which are used in thephysical layer and correspond to REs which do not carry informationoriginated from a higher layer. For example, physical uplink sharedchannel (PUSCH), physical uplink control channel (PUCCH), and physicalrandom access channel (PRACH) are defined as UL physical channels, and ademodulation reference signal (DMRS) for a UL control/data signal, and asounding reference signal (SRS) used for UL channel measurement aredefined as UL physical signals.

In the present disclosure, the PDCCH/PCFICH/PHICH/PDSCH refers to a setof time-frequency resources or a set of REs, which carry downlinkcontrol information (DCI)/a control format indicator (CFI)/a DLacknowledgement/negative acknowledgement (ACK/NACK)/DL data. Further,the PUCCH/PUSCH/PRACH refers to a set of time-frequency resources or aset of REs, which carry UL control information (UCI)/UL data/a randomaccess signal. In the present disclosure, particularly a time-frequencyresource or an RE which is allocated to or belongs to thePDCCH/PCFICH/PHICH/PDSCH/PUCCH/PUSCH/PRACH is referred to as a PDCCHRE/PCFICH RE/PHICH RE/PDSCH RE/PUCCH RE/PUSCH RE/PRACH RE or a PDCCHresource/PCFICH resource/PHICH resource/PDSCH resource/PUCCHresource/PUSCH resource/PRACH resource. Hereinbelow, if it is said thata UE transmits a PUCCH/PUSCH/PRACH, this means that UCI/UL data/a randomaccess signal is transmitted on or through the PUCCH/PUSCH/PRACH.Further, if it is said that a gNB transmits a PDCCH/PCFICH/PHICH/PDSCH,this means that DCI/control information is transmitted on or through thePDCCH/PCFICH/PHICH/PDSCH.

Hereinbelow, an orthogonal frequency division multiplexing (OFDM)symbol/carrier/subcarrier/RE to which a CRS/DMRS/CSI-RS/SRS/UE-RS isallocated to or for which the CRS/DMRS/CSI-RS/SRS/UE-RS is configured isreferred to as a CRS/DMRS/CSI-RS/SRS/UE-RS symbol/carrier/subcarrier/RE.For example, an OFDM symbol to which a tracking RS (TRS) is allocated orfor which the TRS is configured is referred to as a TRS symbol, asubcarrier to which a TRS is allocated or for which the TRS isconfigured is referred to as a TRS subcarrier, and an RE to which a TRSis allocated or for which the TRS is configured is referred to as a TRSRE. Further, a subframe configured to transmit a TRS is referred to as aTRS subframe. Further, a subframe carrying a broadcast signal isreferred to as a broadcast subframe or a PBCH subframe, and a subframecarrying a synchronization signal (SS) (e.g., a primary synchronizationsignal (PSS) and/or a secondary synchronization signal (SSS)) isreferred to as an SS subframe or a PSS/SSS subframe. An OFDMsymbol/subcarrier/RE to which a PSS/SSS is allocated or for which thePSS/SSS is configured is referred to as a PSS/SSS symbol/subcarrier/RE.

In the present disclosure, a CRS port, a UE-RS port, a CSI-RS port, anda TRS port refer to an antenna port configured to transmit a CRS, anantenna port configured to transmit a UE-RS, an antenna port configuredto transmit a CSI-RS, and an antenna port configured to transmit a TRS,respectively. Antenna port configured to transmit CRSs may bedistinguished from each other by the positions of REs occupied by theCRSs according to CRS ports, antenna ports configured to transmit UE-RSsmay be distinguished from each other by the positions of REs occupied bythe UE-RSs according to UE-RS ports, and antenna ports configured totransmit CSI-RSs may be distinguished from each other by the positionsof REs occupied by the CSI-RSs according to CSI-RS ports. Therefore, theterm CRS/UE-RS/CSI-RS/TRS port is also used to refer to a pattern of REsoccupied by a CRS/UE-RS/CSI-RS/TRS in a predetermined resource area.

FIG. 1 illustrates control-plane and user-plane protocol stacks in aradio interface protocol architecture conforming to a 3GPP wirelessaccess network standard between a UE and an evolved UMTS terrestrialradio access network (E-UTRAN). The control plane is a path in which theUE and the E-UTRAN transmit control messages to manage calls, and theuser plane is a path in which data generated from an application layer,for example, voice data or Internet packet data is transmitted.

A physical (PHY) layer at layer 1 (L1) provides information transferservice to its higher layer, a medium access control (MAC) layer. ThePHY layer is connected to the MAC layer via transport channels. Thetransport channels deliver data between the MAC layer and the PHY layer.Data is transmitted on physical channels between the PHY layers of atransmitter and a receiver. The physical channels use time and frequencyas radio resources. Specifically, the physical channels are modulated inorthogonal frequency division multiple access (OFDMA) for downlink (DL)and in single carrier frequency division multiple access (SC-FDMA) foruplink (UL).

The MAC layer at layer 2 (L2) provides service to its higher layer, aradio link control (RLC) layer via logical channels. The RLC layer at L2supports reliable data transmission. RLC functionality may beimplemented in a function block of the MAC layer. A packet dataconvergence protocol (PDCP) layer at L2 performs header compression toreduce the amount of unnecessary control information and thusefficiently transmit Internet protocol (IP) packets such as IP version 4(IPv4) or IP version 6 (IPv6) packets via an air interface having anarrow bandwidth.

A radio resource control (RRC) layer at the lowest part of layer 3 (orL3) is defined only on the control plane. The RRC layer controls logicalchannels, transport channels, and physical channels in relation toconfiguration, reconfiguration, and release of radio bearers. A radiobearer refers to a service provided at L2, for data transmission betweenthe UE and the E-UTRAN. For this purpose, the RRC layers of the UE andthe E-UTRAN exchange RRC messages with each other. If an RRC connectionis established between the UE and the E-UTRAN, the UE is in RRCConnected mode and otherwise, the UE is in RRC Idle mode. A Non-AccessStratum (NAS) layer above the RRC layer performs functions includingsession management and mobility management.

DL transport channels used to deliver data from the E-UTRAN to UEsinclude a broadcast channel (BCH) carrying system information, a pagingchannel (PCH) carrying a paging message, and a shared channel (SCH)carrying user traffic or a control message. DL multicast traffic orcontrol messages or DL broadcast traffic or control messages may betransmitted on a DL SCH or a separately defined DL multicast channel(MCH). UL transport channels used to deliver data from a UE to theE-UTRAN include a random access channel (RACH) carrying an initialcontrol message and a UL SCH carrying user traffic or a control message.Logical channels that are defined above transport channels and mapped tothe transport channels include a broadcast control channel (BCCH), apaging control channel (PCCH), a Common Control Channel (CCCH), amulticast control channel (MCCH), a multicast traffic channel (MTCH),etc.

FIG. 2 illustrates physical channels and a general method fortransmitting signals on the physical channels in the 3GPP system.

Referring to FIG. 2, when a UE is powered on or enters a new cell, theUE performs initial cell search (S201). The initial cell search involvesacquisition of synchronization to an eNB. Specifically, the UEsynchronizes its timing to the eNB and acquires a cell identifier (ID)and other information by receiving a primary synchronization channel(P-SCH) and a secondary synchronization channel (S-SCH) from the eNB.Then the UE may acquire information broadcast in the cell by receiving aphysical broadcast channel (PBCH) from the eNB. During the initial cellsearch, the UE may monitor a DL channel state by receiving a downlinkreference signal (DL RS).

After the initial cell search, the UE may acquire detailed systeminformation by receiving a physical downlink control channel (PDCCH) andreceiving a physical downlink shared channel (PDSCH) based oninformation included in the PDCCH (S202).

If the UE initially accesses the eNB or has no radio resources forsignal transmission to the eNB, the UE may perform a random accessprocedure with the eNB (S203 to S206). In the random access procedure,the UE may transmit a predetermined sequence as a preamble on a physicalrandom access channel (PRACH) (S203 and S205) and may receive a responsemessage to the preamble on a PDCCH and a PDSCH associated with the PDCCH(S204 and S206). In the case of a contention-based RACH, the UE mayadditionally perform a contention resolution procedure.

After the above procedure, the UE may receive a PDCCH and/or a PDSCHfrom the eNB (S207) and transmit a physical uplink shared channel(PUSCH) and/or a physical uplink control channel (PUCCH) to the eNB(S208), which is a general DL and UL signal transmission procedure.Particularly, the UE receives downlink control information (DCI) on aPDCCH. Herein, the DCI includes control information such as resourceallocation information for the UE. Different DCI formats are definedaccording to different usages of DCI.

Control information that the UE transmits to the eNB on the UL orreceives from the eNB on the DL includes a DL/UL acknowledgment/negativeacknowledgment (ACK/NACK) signal, a channel quality indicator (CQI), aprecoding matrix index (PMI), a rank indicator (RI), etc. In the 3GPPLTE system, the UE may transmit control information such as a CQI, aPMI, an RI, etc. on a PUSCH and/or a PUCCH.

FIG. 3 illustrates a structure of a radio frame used in the LTE system.

Referring to FIG. 3, a radio frame is 10 ms (327200×T_(s)) long anddivided into 10 equal-sized subframes. Each subframe is lms long andfurther divided into two slots. Each time slot is 0.5 ms (15360×T_(s))long. Herein, T_(s) represents a sampling time and T_(s)=1/(15kHz×2048)=3.2552×10⁻⁸ (about 33 ns). A slot includes a plurality ofOrthogonal Frequency Division Multiplexing (OFDM) symbols or SC-FDMAsymbols in the time domain by a plurality of Resource Blocks (RBs) inthe frequency domain. In the LTE system, one RB includes 12 subcarriersby 7 (or 6) OFDM symbols. A unit time during which data is transmittedis defined as a Transmission Time Interval (TTI). The TTI may be definedin units of one or more subframes. The above-described radio framestructure is purely exemplary and thus the number of subframes in aradio frame, the number of slots in a subframe, or the number of OFDMsymbols in a slot may vary.

FIG. 4 illustrates a structure of a radio frame used in NR.

In NR, UL and DL transmissions are configured in frames. The radio framehas a length of 10 ms and is defined as two 5-ms half-frames (HF). Thehalf-frame is defined as five 1 ms subframes (SF). A subframe is dividedinto one or more slots, and the number of slots in a subframe depends onsubcarrier spacing (SCS). Each slot includes 12 or 14 OFDM(A) symbolsaccording to a cyclic prefix (CP). When a normal CP is used, each slotincludes 14 symbols. When an extended CP is used, each slot includes 12symbols. Here, the symbols may include OFDM symbols (or CP-OFDM symbols)and SC-FDMA symbols (or DFT-s-OFDM symbols).

In the NR system, the OFDM(A) numerology (e.g., SCS, CP length, etc.)may be configured differently among a plurality of cells merged for oneUE. Thus, the (absolute time) duration of a time resource (e.g., SF,slot or TTI) (referred to as a time unit (TU) for simplicity) composedof the same number of symbols may be set differently among the mergedcells.

FIG. 5 illustrates a slot structure of an NR frame. A slot includes aplurality of symbols in the time domain. For example, in the case of thenormal CP, one slot includes seven symbols. On the other hand, in thecase of the extended CP, one slot includes six symbols. A carrierincludes a plurality of subcarriers in the frequency domain. A resourceblock (RB) is defined as a plurality of consecutive subcarriers (e.g.,12 consecutive subcarriers) in the frequency domain. A bandwidth part(BWP) is defined as a plurality of consecutive (P)RBs in the frequencydomain and may correspond to one numerology (e.g., SCS, CP length,etc.). A carrier may include up to N (e.g., five) BWPs. Datacommunication is performed through an activated BWP, and only one BWPmay be activated for one UE. In the resource grid, each element isreferred to as a resource element (RE), and one complex symbol may bemapped thereto.

FIG. 6 illustrates a structure of a self-contained slot. In the NRsystem, a frame has a self-contained structure in which a DL controlchannel, DL or UL data, a UL control channel, and the like may all becontained in one slot. For example, the first N symbols (hereinafter, DLcontrol region) in the slot may be used to transmit a DL controlchannel, and the last M symbols (hereinafter, UL control region) in theslot may be used to transmit a UL control channel. N and M are integersgreater than or equal to 0. A resource region (hereinafter, a dataregion) that is between the DL control region and the UL control regionmay be used for DL data transmission or UL data transmission. Forexample, the following configuration may be considered. Respectivesections are listed in a temporal order.

1. DL only configuration

2. UL only configuration

3. Mixed UL-DL configuration

DL region+Guard period (GP)+UL control region

DL control region+GP+UL region

DL region: (i) DL data region, (ii) DL control region+DL data region

UL region: (i) UL data region, (ii) UL data region+UL control region

The PDCCH may be transmitted in the DL control region, and the PDSCH maybe transmitted in the DL data region. The PUCCH may be transmitted inthe UL control region, and the PUSCH may be transmitted in the UL dataregion. Downlink control information (DCI), for example, DL datascheduling information, UL data scheduling information, and the like,may be transmitted on the PDCCH. Uplink control information (UCI), forexample, ACK/NACK information about DL data, channel state information(CSI), and a scheduling request (SR), may be transmitted on the PUCCH.The GP provides a time gap in the process of the UE switching from thetransmission mode to the reception mode or from the reception mode tothe transmission mode. Some symbols at the time of switching from DL toUL within a subframe may be configured as the GP.

In an NR system, a technique of using an ultra-high frequency band, thatis, a millimeter frequency band at or above 6 GHz is considered in orderto transmit data to a plurality of users at a high transmission rate ina wide frequency band. In 3GPP, this technique is called NR and will bereferred to as an NR system in the present disclosure. However, themillimeter frequency band has the frequency property that a signal isattenuated too rapidly according to distance due to the use of too higha frequency band. Accordingly, the NR system using a frequency band ator above at least 6 GHz employs a narrow beam transmission scheme inwhich a signal is transmitted with concentrated energy in a specificdirection, not omni-directionally, to thereby compensate for rapidpropagation attenuation and thus overcome decrease of coverage caused bythe rapid propagation attenuation. However, if a service is provided byusing only one narrow beam, the service coverage of one gNB becomesnarrow, and thus the gNB provides a service in a wide band by collectinga plurality of narrow beams.

As a wavelength becomes short in the millimeter frequency band, that is,millimeter wave (mmW) band, it is possible to install a plurality ofantenna elements in the same area. For example, a total of 100 antennaelements may be installed at (wavelength) intervals of 0.5 lambda in a30-GHz band with a wavelength of about 1 cm in a two-dimensional (2D)array on a 5 cm by 5 cm panel. Therefore, it is considered to increasecoverage or throughput by increasing beamforming gain through use of aplurality of antenna elements in mmW.

To form a narrow beam in the millimeter frequency band, a beamformingscheme is mainly considered, in which a gNB or a UE transmits the samesignals with appropriate phase differences through multiple antennas, tothereby increase energy only in a specific direction. Such beamformingschemes include digital beamforming for generating a phase differencebetween digital baseband signals, analog beamforming for generating aphase difference between modulated analog signals by using time delay(i.e., a cyclic shift), and hybrid beamforming using both digitalbeamforming and analog beamforming. If a transceiver unit (TXRU) isprovided to enable control of transmission power and a phase per antennaelement, independent beamforming per frequency resource is possible.However, installation of TXRUs for all of about 100 antenna elements isnot feasible in terms of cost. That is, to compensate for rapidpropagation attenuation in the millimeter frequency band, multipleantennas should be used, and digital beamforming requires as many radiofrequency (RF) components (e.g., digital to analog converters (DACs),mixers, power amplifiers, and linear amplifiers) as the number ofantennas. Accordingly, implementation of digital beamforming in themillimeter frequency band faces the problem of increased cost ofcommunication devices. Therefore, in the case in which a large number ofantennas is required as in the millimeter frequency band, analogbeamforming or hybrid beamforming is considered. In analog beamforming,a plurality of antenna elements is mapped to one TXRU, and the directionof a beam is controlled by an analog phase shifter. A shortcoming ofthis analog beamforming scheme is that frequency selective beamforming(BF) cannot be provided because only one beam direction can be producedin a total band. Hybrid BF stands between digital BF and analog BF, inwhich B TXRUs fewer than Q antenna elements are used. In hybrid BF, thedirections of beams transmittable at the same time are limited to B orbelow although the number of beam directions is different according toconnections between B TXRUs and Q antenna elements.

Digital BF performs signal processing on a digital baseband signal thatis to be transmitted or is received as mentioned above, and thereforedigital BF may transmit or receive signals in multiple directions at thesame time using multiple beams. In contrast, analog BF performsbeamforming with a received analog signal or an analog signal to betransmitted in a modulated state, and therefore analog BF may notsimultaneously transmit or receive signals in multiple directions beyondthe range covered by one beam. In general, a gNB communicates withmultiple users at the same time using broadband transmission ormulti-antenna characteristics. When the gNB uses analog or hybrid BF andforms an analog beam in one beam direction, the gNB is allowed tocommunicate only with users included in the same analog beam directiondue to the characteristics of analog BF. An RACH resource allocationscheme and a scheme of resource utilization in the gNB according to thepresent disclosure to be described later are proposed in considerationof constraints resulting from the characteristics of analog BF or hybridBF.

FIG. 7 abstractly illustrates a hybrid beamforming structure in terms ofTXRUs and physical antennas.

For the case in which multiple antennas are used, hybrid BF with digitalBF and analog BF in combination has emerged. Analog BF (or RF BF) is anoperation of performing precoding (or combining) in a transceiver (RFunit). Due to precoding (combining) in each of a baseband unit and atransceiver (or an RF unit), hybrid BF offers the benefit of performanceclose to the performance of digital BF, while reducing the number of RFchains and the number of DACs (or analog to digital converters (ADCs).For convenience, a hybrid BF structure may be represented by N TXRUs andM physical antennas. Digital BF for L data layers to be transmitted by atransmission end may be represented as an N-by-L matrix, and then Nconverted digital signals are converted into analog signals throughTXRUs and subjected to analog BF represented as an M-by-N matrix.

In FIG. 7, the number of digital beams is L, and the number of analogbeams is N. Further, it is considered in the NR system that a gNB isconfigured to change analog BF on a symbol basis so as to moreefficiently support BF for a UE located in a specific area. Further,when one antenna panel is defined by N TXRUs and M RF antennas,introduction of a plurality of antenna panels to which independenthybrid BF is applicable is also considered. As such, in the case inwhich a gNB uses a plurality of analog beams, a different analog beammay be preferred for signal reception at each UE. Therefore, a beamsweeping operation is under consideration, in which for at least an SS,system information, and paging, a gNB changes a plurality of analogbeams on a symbol basis in a specific slot or SF to allow all UEs tohave reception opportunities.

FIG. 8 illustrates a beam sweeping operation for an SS and systeminformation during DL transmission.

In FIG. 8, physical resources or a physical channel which broadcastssystem information of the NR system is referred to as an xPBCH. Analogbeams from different antenna panels may be transmitted simultaneously inone symbol, and introduction of a beam reference signal (BRS)transmitted for a single analog beam corresponding to a specific antennapanel as illustrated in FIG. 8 is under discussion in order to measure achannel per analog beam. BRSs may be defined for a plurality of antennaports, and each antenna port of the BRSs may correspond to a singleanalog beam. Unlike the BRSs, the SS or the xPBCH may be transmitted forall analog beams included in an analog beam group so that any UE mayreceive the SS or the xPBCH successfully.

FIG. 9 illustrates a cell in an NR system.

Referring to FIG. 9, compared to a wireless communication system such aslegacy LTE in which one eNB forms one cell, configuration of one cell bya plurality of transmission/reception points (TRPs) is under discussionin the NR system. If a plurality of TRPs forms one cell, even though aTRP serving a UE is changed, seamless communication is advantageouslypossible, thereby facilitating mobility management for UEs.

Compared to the LTE/LTE-A system in which a PSS/SSS is transmittedomnidirectionally, a method of transmitting a signal such as aPSS/SSS/PBCH through BF performed by sequentially switching a beamdirection to all directions at a gNB applying mmWave is considered.Signal transmission/reception performed by switching a beam direction isreferred to as beam sweeping or beam scanning. In the presentdisclosure, “beam sweeping” is a behavior of a transmission side, and“beam scanning” is a behavior of a reception side. For example, if up toN beam directions are available to the gNB, the gNB transmits a signalsuch as a PSS/SSS/PBCH in the N beam directions. That is, the gNBtransmits an SS such as the PSS/SSS/PBCH in each direction by sweeping abeam in directions available to or supported by the gNB. Alternatively,if the gNB is capable of forming N beams, some beams may be grouped intoone beam group, and the PSS/SSS/PBCH may be transmitted/received on agroup basis. One beam group includes one or more beams. Signals such asthe PSS/SSS/PBCH transmitted in the same direction may be defined as oneSS block (SSB), and a plurality of SSBs may exist in one cell. If aplurality of SSBs exist, an SSB index may be used to identify each SSB.For example, if the PSS/SSS/PBCH is transmitted in 10 beam directions inone system, the PSS/SSS/PBCH transmitted in the same direction may forman SSB, and it may be understood that 10 SSBs exist in the system. Inthe present disclosure, a beam index may be interpreted as an SSB index

Currently, in 3GPP Release 16, i.e., standardization of an NR system, arelay gNB is under discussion for the purpose of reducing wiredconnection between gNBs while compensating for a coverage hole. This isimplemented through integrated access and backhaul (IAB). A donor gNB(DgNB) transmits a signal to a UE via a relay gNB. IAB includes awireless backhaul link for communication between a DgNB and a relay gNBor between relay gNBs and an access link for communication between aDgNB and a UE or between a relay gNB and a UE.

Signal transmission through IAB is broadly categorized into twoscenarios. The first one is an in-band scenario in which a wirelessbackhaul link and an access link use the same frequency band, and thesecond one is an out-band scenario in which the wireless backhaul linkand the access link use different frequency bands. The first scenarioshould also deal with interference between the wireless backhaul linkand the access link compared to the second scenario, so that the firstscenario may be lower than the second scenario in terms of feasibilityof implementation.

In the current standardization of the NR system, it is assumed thatnodes transmit an SSB or a CSI-RS on the backhaul link in order toperform a discovery procedure. IAB nodes measure or discover the SSB orthe CSI-RS to feed back the SSB or the CSI-RS to a parent node or adonor node, and a network or middle nodes determine route selectionbased on the feedback result. When root selection is determined by themiddle nodes, the parent node may relay the discovered or measuredfeedback result to the middle nodes. When the network is responsible forroot selection for nodes that the network manages, the parent noderelays the discovered or measured feedback result to the donor node.

This discovery operation is based on the assumption that IAB nodesoperate in a half-duplex scheme which does not allow simultaneoustransmission and reception for the IAB nodes. Accordingly, there is aproblem in that, while an IAB node transmits an SSB or a CSI-RS for thediscovery operation, the IAB node is incapable of measuring ordiscovering SSBs or CSI-RSs that other nodes transmit. To solve thisproblem, it is necessary to perform TDM on SSBs or CSI-RSs transmittedbetween nodes. To this end, a transmission pattern for transmission ofthe SSBs or the CSI-RSs or a muting pattern for discontinuing ongoingtransmission and discovering or measuring discovery signals from othernodes may be needed.

Hereinbelow, for convenience of description, when RN1 and RN2 connectedvia a backhaul link are present and RN1 relays transmitted and receiveddata to RN2, RN1 will be referred to as a parent node of RN2, and RN2will be referred to as a child node RN of RN1.

A discovery signal described in the present disclosure refers to asignal transmitted by an IAB node and is transmitted to enable other IABnodes or UEs to find or discover the IAB node. The discovery signal mayhave a type of SSB, a type of CSI-RS, or a type of signal introduced inlegacy NR. While the present disclosure mainly describes the case inwhich an IAB node discovers other IAB nodes, the present disclosure mayalso be applied to the case in which the UE discovers IAB nodes.

In order for IAB nodes to set DL transmission timings thereof in an IABscenario, the present disclosure assumes that a parent node controls theDL transmission timings. Upon controlling a DL transmission timing of achild node of the parent node, the parent node may use a timing advance(TA) assumed when the child node transmits a UL signal to the parentnode and the TA is a value for UL transmission. However, when the TAvalue is updated, there is a problem in that a DL transmission timing isalso updated. The present disclosure proposes a method for solving thisproblem.

Currently, in NR standardization, a DL transmission timing for each IABnode is determined by advancing the DL transmission timing by apropagation delay (PD) from a parent node of the IAB node to the IABnode from a DL reception timing from the parent node. That is, fornetwork synchronization, IAB nodes perform DL transmission at the sametiming, thereby reducing interference between IAB nodes or between UEs.

To calculate a PD from a parent node to an IAB node, the IAB node mayuse a TA value received from the parent node. Usually, the TA value istwice the PD and is used when calculating a UL transmission timing tothe parent node. The IAB node performs UL transmission by advancing thetransmission timing by the TA from the DL reception timing, therebycompensating for both a PD during UL transmission from the IAB node tothe parent node and a PD during DL reception.

Most simply, if the DL transmission timing is calculated by advancingthe DL transmission timing by TA/2 from the DL reception timing, the IABnode may perform DL transmission at the same timing as the parent node.However, for convenience, the TA value may be set to a value slightlydifferent from a double value of an actual PD.

FIG. 10 is a diagram illustrating a method of determining a DLtransmission timing of an IAB node.

Referring to FIG. 10, a child node generally determines a ULtransmission timing thereof by advancing the UL transmission timing by avalue obtained by adding a TA offset value set for each band to a TAvalue received from a parent node from a reception timing of a DL signalof a parent node.

In this case, the parent node may calculate the TA value by doubling aPD value between the parent node and the child node on the assumptionthat the TA offset value set for each band is a switching time. However,the parent node may calculate the TA value as a value other than twicethe PD, on the assumption that the switching time is different from theTA offset value, and inform the child node of the calculated TA value.In this case, the difference value, i.e., switching time-TA offsetvalue, may be added to 2*PD and the resultant value may be determined asthe TA value.

Accordingly, the parent node should inform the child node of acorrection term in addition to the TA value in order to control the DLtransmission timing. Two methods may be considered for the DLtransmission timing.

1. For correction of the DL transmission timing, the parent node sets anX value for the child node.

(a) First, when the child node calculates the DL transmission timing,the DL transmission timing is advanced by X from the DL receptiontiming. That is, the parent node informs the child node of X at a timeregardless of setting of a TA so that the child node sets the DLtransmission timing. This method is advantageous in that the DLtransmission timing is not changed since there is no relation to the TAeven if the TA is changed. However, the range of the X value increasesby the amount of the TA. The X value may be indicated by a random accessresponse (RAR) or by RRC signaling.

In this case, the child node applies the DL transmission timing byadvancing the DL transmission timing by X from the DL reception timingof the parent node.

(b) Alternatively, when the child node calculates the DL transmissiontiming, the DL transmission timing is advanced by TA/2+X from the DLreception timing. That is, the parent node informs the child node ofonly a correction term between a TA offset and a switching time as Xbased on setting of the TA, so that the child node sets the DLtransmission timing by adding the correction term to TA/2. This methodis advantageous in that the range of X decreases because only thecorrection term is set to X based on TA/2. However, since the DLtransmission timing is dependent on the TA value, the DL transmissiontiming is also updated each time the TA value is updated, so that the DLtransmission timing may be continuously changed. The X value may beindicated by RRC signaling or a MAC control element (CE).

In this case, the child node applies the DL transmission timing byadvancing the DL transmission timing by TA/2+X from the DL receptiontiming of the parent node.

When the child node calculates the DL transmission timing described in(b) of Method 1, the method of advancing the DL transmission timing byTA/2+X from the DL reception timing has a disadvantage in that the DLtransmission timing is continuously changed whenever the TA is updated.This means that the timing value is continuously changed when DLtransmission to a UE supported by the child node or to a lower childnode of the child node is performed. To solve such a problem, thefollowing method may be used.

When the child node a configuration is configured with a DL transmissiontiming value received from the parent node, it is assumed that the DLtransmission timing value is not affected by a TA value updated underthe following conditions.

(i) The DL transmission timing is always not affected. That is, it maybe assumed that only an initial TA value indicated by an RAR receivedduring contention-based RACH transmission is applied to the DLtransmission timing. In addition, when updating the DL transmissiontiming, it may be assumed that only a correction value based on aprevious DL transmission timing value is indicated and other values arenot applied to the DL transmission timing even when the TA value ischanged.

(ii) When the TA value is changed by the RAR or RRC signaling, it isindicated whether or not the TA value is applied to the DL transmissiontiming.

(iii) The X value indicating that the DL transmission timing is changedis indicated by the RAR or RRC signaling (or MAC CE) so that a currentTA value is applied only when a command to change the DL transmissiontiming is received. In this case, the current TA value means the mostrecently updated TA value.

(iv) When the DL transmission timing is changed by the RAR or RRCsignaling, a TA value is applied to the DL transmission timing based ona TA value used when a previous transmission timing is set.

(v) Whether or not to use the TA value updated when the DL transmissiontiming is changed may be indicated through the RAR or RRC signaling. Ifthe updated TA value is not used, it may be assumed that a TA value isapplied to the DL transmission timing based on the TA value used whenthe previous DL transmission timing is set.

FIG. 11 is a flowchart of a method for performing DL transmissionaccording to an embodiment of the present disclosure.

Referring to FIG. 11, a child node receives information about a TA valuefrom a parent node in step 1101. Thereafter, the child node determines areception timing of a first DL signal transmitted by the parent node instep 1103.

Next, in step 1105, the child node calculates a transmission timing of asecond DL signal by advancing the transmission timing of the second DLsignal by a timing correction value, based on the TA value and a presetoffset value, from the reception timing of the first DL signal. Thetiming correction value means a sum of a half of the TA value and thepreset offset value. This serves to set the transmission timing of thefirst DL signal and the transmission timing of the second DL signal tobe the same.

Finally, in step 1107, the child node transmits the second DL signal toanother child node according to the transmission timing of the second DLsignal.

Desirably, the preset offset value in step 1105 may be included in anRAR message received from the parent node or provided by higher layersignaling. In this case, the TA value may be a most recently updated TAvalue from reception of the preset offset value.

FIG. 12 illustrates an example of a wireless communication deviceaccording to an embodiment of the present disclosure.

The wireless communication device illustrated in FIG. 12 may represent aUE and/or a BS according to an embodiment of the present disclosure.However, the wireless communication device of FIG. 12 may be replacedwith any of various types of devices such as a vehicle communicationsystem or device, a wearable device, and a laptop, not limited to the UEand/or the BS according to the embodiment of the present disclosure.More specifically, the above device may be a BS, a network node, a TxUE, an Rx UE, a wireless device, a wireless communication device, avehicle, a vehicle having a self-driving function, an unmanned aerialvehicle (UAV), an artificial intelligence (AI) module, a robot, anaugmented reality (AR) device, a virtual reality (VR) device, amachine-type communication (MTC) device, an Internet of things (IoT)device, a medical device, a FinTech device (or a financial device), asecurity device, a weather/environment device, or a device related tothe fourth industrial revolution or a 5G service. The UAV may be, forexample, an aircraft without a human being onboard, which aviates by awireless control signal. The MTC device and the IoT device may be, forexample, devices that do not require direct human intervention ormanipulation and may include smartmeters, vending machines,thermometers, smartbulbs, door locks, or various sensors. The medicaldevice may be, for example, a device used for the purpose of diagnosing,treating, relieving, curing, or preventing disease or a device used forthe purpose of inspecting, replacing, or modifying a structure or afunction and may include a device for treatment, a device for operation,a device for (in vitro) diagnosis, a hearing aid, or an operationdevice. The security device may be, for example, a device installed toprevent a danger that may arise and to maintain safety and may include acamera, a CCTV, or a black box. The FinTech device may be, for example,a device capable of providing a financial service such as mobile paymentand may include a payment device or a point of sale (POS) system. Theweather/environment device may be, for example, a device for monitoringor predicting a weather/environment.

The Tx UE or the Rx UE may include, for example, a cellular phone, asmartphone, a laptop computer, a digital broadcast terminal, a personaldigital assistant (PDA), a portable multimedia player (PMP), anavigation system, a slate PC, a tablet PC, an ultrabook, a wearabledevice (e.g., a smartwatch, smartglasses, or a head mounted display(HMD)), or a foldable device. The HMD may be, for example, a type ofdisplay device that is worn on the head and may be used to implement VRor AR.

In the example of FIG. 12, the UE and/or the BS according to theembodiment of the present disclosure includes at least one processor 10such as a digital signal processor or a microprocessor, a transceiver35, a power management module 5, an antenna 40, a battery 55, a display15, a keypad 20, a memory 30, a subscriber identity module (SIM) card25, a speaker 45, and a microphone 50. In addition, the UE and/or the BSmay include one or more antennas. The transceiver 35 may be alsoreferred to as an RF module.

The at least one processor 10 may be configured to implement thefunctions, procedures and/or methods. In at least some of theembodiments, the at least one processor 10 may implement one or moreprotocols, such as layers of radio interface protocols (e.g., functionallayers).

The memory 30 is coupled to the at least one processor 10 and storesinformation related to the operations of the at least one processor 10.The memory 30 may be located inside or outside the at least oneprocessor 10 and may be coupled to the at least one processor 10 byvarious techniques such as wired or wireless communication.

A user may input various types of information (e.g., indicationinformation such as a telephone number) by various techniques such aspressing a button on the keypad 20 or activating voice using themicrophone 50. The at least one processor 10 executes appropriatefunctions such as receiving and/or processing information of the userand dialing a telephone number.

It is also possible to retrieve data (e.g., operational data) from theSIM card 25 or the memory 30 to execute the appropriate functions. Inaddition, the at least one processor 10 may receive and process globalpositioning system (GPS) information from a GPS chip to obtain locationinformation about the UE and/or the BS such as in vehicle navigation,map service, or the like, or execute functions related to the locationinformation. Further, the at least one processor 10 may display thesevarious types of information and data on the display 15 for referenceand user convenience.

The transceiver 35 is coupled to the at least one processor 10 totransmit and/or receive wireless signals such as RF signals. The atleast one processor 10 may control the transceiver 35 to initiatecommunication and transmit wireless signals including various types ofinformation or data, such as voice communication data. The transceiver35 may include a receiver for receiving a wireless signal and atransmitter for transmitting a wireless signal. The antenna 40facilitates the transmission and reception of wireless signals. In someembodiments, upon receipt of a wireless signal, the transceiver 35 mayforward and convert the signal to a baseband frequency for processing bythe at least one processor 10. The processed signal may be processedaccording to various techniques, such as being converted into audible orreadable information, and output through the speaker 45.

In some embodiments, a sensor may also be coupled to the at least oneprocessor 10. The sensor may include one or more sensing devicesconfigured to detect various types of information, including velocity,acceleration, light, vibration, and the like. The at least one processor10 receives and processes sensor information obtained from the sensor,such as proximity, position, image, and the like, thereby executingvarious functions such as collision avoidance and autonomous driving.

Various components such as a camera and a universal serial bus (USB)port may further be included in the UE and/or the BS. For example, acamera may further be coupled to the at least one processor 10, for usein various services including autonomous driving and vehicle safetyservices.

FIG. 12 merely illustrates one example of devices included in a UEand/or a BS, not limiting the present disclosure. For example, somecomponents, such as the keypad 20, the GPS chip, the sensor, the speaker45 and/or the microphone 50 may be excluded from UE and/or BSimplementation in some embodiments.

FIG. 13 illustrates an AI device 100 according to an embodiment of thepresent disclosure.

The AI device 100 may be implemented by a stationary or mobile device,for example, a TV, a projector, a mobile phone, a smartphone, a desktopcomputer, a laptop computer, a digital broadcasting terminal, a personaldigital assistant (PDA), a portable multimedia player (PMP), anavigation device, a tablet PC, a wearable device, a set-top box (STB),a digital multimedia broadcasting (DMB) receiver, a radio, a washingmachine, a refrigerator, a desktop computer, a digital signage, a robot,a vehicle, etc.

Referring to FIG. 13, the AI device 100 may include a communication unit110, an input unit 120, a learning processor 130, a sensing unit 140, anoutput unit 150, a memory 170, and a processor 180.

The communication unit 110 may transmit and receive data to and fromexternal devices such as an AI server 200 and other AI devices 100 a to100 e based on wired or wireless communication technology. For example,the communication unit 110 may transmit and receive sensor information,user inputs, learning models, and control signals to and from theexternal devices.

The communication technology used by the communication unit 110 includesGlobal System for Mobile communication (GSM), Code Division MultipleAccess (CDM), Long Term Evolution (LTE), 5G Wireless Local Area Network(WLAN), Wireless Fidelity (Wi-Fi), Bluetooth™, Radio FrequencyIdentification (RFID), Infrared Data Association (IrDA), ZigBee, NearField Communication (NFC), etc.

The input unit 120 may obtain various types of data.

The input unit 120 may include a camera for inputting a video signal, amicrophone for receiving an audio signal, and a user input unit forreceiving information from a user. The camera or microphone may betreated as a sensor, and the signal obtained from the camera ormicrophone may be considered as sensing data or sensor information.

The input unit 120 may obtain learning data for a learning model andinput data to be used when an output is obtained based on the learningmodel. The input unit 120 may obtain raw input data. In this case, theprocessor 180 or learning processor 130 may extract an input feature bypreprocessing the input data.

The learning processor 130 may train a model configured with an ANNbased on the learning data. Here, the trained ANN may be referred to asthe learning model. The learning model may be used to infer a resultvalue for new input data rather than the learning data, and the inferredvalue may be used as a basis for determining whether to perform acertain operation.

In this case, the learning processor 130 may perform AI processingtogether with a learning processor 240 of the AI server 200.

The learning processor 130 may include a memory integrated with orimplemented in the AI device 100. Alternatively, the learning processor130 may be implemented with the memory 170, an external memory directlycoupled to the AI device 100, or a memory in an external device.

The sensing unit 140 may obtain at least one of internal information ofthe AI device 100, surrounding environment information of the AI device100, and user information using various sensors.

The sensor included in the sensing unit 140 may include a proximitysensor, an illumination sensor, an acceleration sensor, a magneticsensor, a gyro sensor, an inertial sensor, an RGB sensor, an IR sensor,a fingerprint recognition sensor, an ultrasonic sensor, an opticalsensor, a microphone, a LIDAR, a radar, and the like.

The output unit 150 may generate an output related to visual, audible,or tactile sense.

The output unit 150 may include a display unit for outputting visualinformation, a speaker for outputting audible information, a hapticmodule for outputting tactile information, and the like.

The memory 170 may store data supporting various functions of the Aldevice 100. For example, the memory 170 may store input data, learningdata, learning models, learning histories, etc. obtained by the inputunit 120.

The processor 180 may determine at least one executable operation of theAI device 100 based on information determined or generated by a dataanalysis algorithm or machine learning algorithm. The processor 180 maycontrol the components of the AI device 100 to perform the determinedoperation.

To this end, the processor 180 may request, search for, receive, oremploy data of the learning processor 130 or memory 170 and control thecomponents of the AI device 100 to execute an expected or preferableoperation or among the one or more executable operations.

If the processor 180 requires association with an external device toperform the determined operation, the processor 180 may generate acontrol signal for controlling the corresponding external device andtransmit the generated control signal to the external device.

The processor 180 may obtain intention information from a user input anddetermine the intention of the user based on the obtained intentioninformation.

In this case, the processor 180 may obtain the intention informationcorresponding to the user input using at least one of a speech-to-text(STT) engine for converting a voice input into a character string or anatural language processing (NLP) engine for obtaining intentioninformation from a natural language.

At least one of the STT engine and the NLP engine may be configured withthe ANN of which at least a part is trained according to the machinelearning algorithm. At least one of the STT engine and the NLP enginemay be trained by the learning processor 130, by the learning processor240 of the AI server 200, or by distributed processing thereof.

The processor 180 may collect history information including userfeedback on the operation of the AI device 100 and details thereof. Theprocessor 180 may store the history information in the memory 170 orlearning processor 130 or transmit the history information to anexternal device such as the AI server 200. The collected historyinformation may be used to update the learning model.

The processor 180 may control at least some of the components of the AIdevice 100 to drive an application program stored in the memory 170.Further, the processor 180 may operate two or more of the componentsincluded in the AI device 100 in combination to drive the applicationprogram.

FIG. 14 illustrates the AI server 200 according to an embodiment of thepresent disclosure.

Referring to FIG. 14, the AI server 200 may mean a device for trainingan ANN based on a machine learning algorithm or a device for using atrained ANN. Here, the AI server 200 may include a plurality of serversto perform distributed processing or may be defined as a 5G network. TheAI server 200 may be included as a part of the AI device 100 to performat least part of AI processing together.

The AI server 200 may include a communication unit 210, a memory 230,the learning processor 240, a processor 260, and the like.

The communication unit 210 may transmit and receive data to and from anexternal device such as the AI device 100.

The memory 230 may include a model storage unit 231. The model storageunit 231 may store a model being trained or trained (or an ANN 231 a)through the learning processor 240.

The learning processor 240 may train the ANN 231 a based on learningdata. The ANN, i.e., a learning model may be included in the AI server200 or in an external device such as the AI device 100.

The learning model may be implemented by hardware, software or acombination thereof. If a part or the entirety of the learning model isimplemented with software, one or more instructions for the learningmodel may be stored in the memory 230.

The processor 260 may infer a result value for new input data based onthe learning model and generate a response or control command based onthe inferred result value.

FIG. 15 illustrates an AI system 1 according to an embodiment of thepresent disclosure.

Referring to FIG. 15, at least one of the AI server 200, a robot 100 a,an autonomous driving vehicle 100 b, an XR device 100 c, a smartphone100 d, and a home appliance 100 e is connected to a cloud server 10 inthe AI system 1. Here, the robot 100 a, the autonomous vehicle 100 b,the XR device 100 c, the smartphone 100 d, or the home appliance 100 e,to which the AI technology is applied, may be referred to as an AIdevice 100 a to 100 e.

The cloud network 10 may refer to a network configuring part of a cloudcomputing infrastructure or a network existing in the cloud computinginfrastructure. Here, the cloud network 10 may be configured with a 3Gnetwork, a 4G or LTE network, or a 5G network.

That is, each of the devices 100 a to 100 e and 200 included in the AIsystem 1 may be connected to each other through the cloud network 10. Inparticular, the devices 100 a to 100 e and 200 may communicate with eachother through a BS or may communicate with each other directly withoutthe BS.

The AI server 200 may include a server in charge of AI processing and aserver in charge of big data computation.

The AI server 200 may be connected to at least one of the robot 100 a,the autonomous vehicle 100 b, the XR device 100 c, the smartphone 100 d,or the home appliance 100 e included in the AI system 1 via the cloudnetwork 10 and help at least part of AI processing of the connected AIdevices 100 a to 100 e.

In this case, the AI server 200 may train an ANN according to a machinelearning algorithm on behalf of the AI devices 100 a to 100 e anddirectly store or transmit a learning model to the AI devices 100 a to100 e.

The AI server 200 may receive input data from the AI devices 100 a to100 e, infer a result value for the received input data based on thelearning model, generate a response or control command based on theinferred result value, and transmit the response or control command tothe AI devices 100 a to 100 e.

Alternatively, the AI devices 100 a to 100 e may directly infer theresult value for the input data based on the learning model and generatethe response or control command based on the inferred result value.

Hereinafter, various embodiments of the AI devices 100 a to 100 e towhich the above-described technology is applied will be described. TheAI devices 100 a to 100 e illustrated in FIG. 15 may be considered as aspecific example of the AI device 100 illustrated in FIG. 14.

AI+Robot

If the AI technology is applied to the robot 100 a, the robot 100 a maybe implemented as a guide robot, a transport robot, a cleaning robot, awearable robot, an entertainment robot, a pet robot, an unmanned flyingrobot, etc.

The robot 100 a may include a robot control module for controlling anoperation, and the robot control module may refer to a software moduleor a chip implemented by hardware.

The robot 100 a may obtain state information of the robot 100 a, detect(recognize) a surrounding environment and objects, generate map data,determine a travel route or driving plan, or determine a response oraction to user interaction by using sensor information obtained fromvarious types of sensors.

To determine the travel route or driving plan, the robot 100 a may usesensor information obtained from at least one of the following sensors:a LIDAR, a radar, and a camera to determine a movement route and atravel plan.

The robot 100 a may perform the above-described operations based on alearning model configured with at least one ANN. For example, the robot100 a may recognize the surrounding environment and objects based on thelearning model and determine an operation based on the recognizedsurrounding environment or object. Here, the learning model may bedirectly trained by the robot 100 a or by an external device such as theAI server 200.

The robot 100 a may operate by directly generating a result based on thelearning model. Alternatively, the robot 100 a may transmit sensorinformation to the external device such as the AI server 200 and receivea result generated based on the sensor information.

The robot 100 a may determine the travel route and driving plan based onat least one of the map data, the object information detected from thesensor information, or the object information obtained from the externaldevice. Then, the robot 100 a may move according to the determinedtravel path and driving plan under control of its driving unit.

The map data may include object identification information about variousobjects placed in a space in which the robot 100 a moves. For example,the map data may include object identification information about fixedobjects such as walls and doors and movable objects such as flower potsand desks. The object identification information may include a name, atype, a distance, a position, etc.

The robot 100 a may operate and move by controlling the driving unitbased on the user control/interaction. In this case, the robot 100 a mayobtain intention information from the motion or speech of the user anddetermine a response based on the obtained intention information.

AI+Autonomous Driving

If the AI technology is applied to the autonomous driving vehicle 100 b,the autonomous driving vehicle 100 b may be implemented as a mobilerobot, a vehicle, an unmanned flying vehicle, etc.

The autonomous driving vehicle 100 b may include an autonomous drivingcontrol module for controlling the autonomous driving function, and theautonomous driving control module may refer to a software module or achip implemented by hardware. The autonomous driving control module maybe included in the autonomous driving vehicle 100 b as a componentthereof, but it may be implemented with separate hardware and connectedto the outside of the autonomous driving vehicle 100 b.

The autonomous driving vehicle 100 b may obtain state information aboutthe autonomous driving vehicle 100 b based on sensor informationacquired from various types of sensors, detect (recognize) a surroundingenvironment and objects, generate map data, determine a travel route anddriving plan, or determine an operation.

Similarly to the robot 100 a, the autonomous driving vehicle 100 b mayuse the sensor information obtained from at least one of the followingsensors: a LIDAR, a radar, and a camera so as to determine the travelroute and driving plan.

In particular, the autonomous driving vehicle 100 b may recognize anenvironment and objects in an area hidden from view or an area over acertain distance by receiving the sensor information from externaldevices. Alternatively, the autonomous driving vehicle 100 b may receiveinformation, which is recognized by the external devices.

The autonomous driving vehicle 100 b may perform the above-describedoperations based on a learning model configured with at least one ANN.For example, the autonomous driving vehicle 100 b may recognize thesurrounding environment and objects based on the learning model anddetermine the driving path based on the recognized surroundingenvironment and objects. The learning model may be trained by theautonomous driving vehicle 100 a or an external device such as the AIserver 200.

The autonomous driving vehicle 100 b may operate by directly generatinga result based on the learning model. Alternatively, the autonomousdriving vehicle 100 b may transmit sensor information to the externaldevice such as the AI server 200 and receive a result generated based onthe sensor information.

The autonomous driving vehicle 100 b may determine the travel route anddriving plan based on at least one of the map data, the objectinformation detected from the sensor information, or the objectinformation obtained from the external device. Then, the autonomousdriving vehicle 100 b may move according to the determined travel pathand driving plan under control of its driving unit.

The map data may include object identification information about variousobjects placed in a space (e.g., road) in which the autonomous drivingvehicle 100 b moves. For example, the map data may include objectidentification information about fixed objects such as street lamps,rocks, and buildings and movable objects such as vehicles andpedestrians. The object identification information may include a name, atype, a distance, a position, etc.

The autonomous driving vehicle 100 b may operate and move by controllingthe driving unit based on the user control/interaction. In this case,the autonomous driving vehicle 100 b may obtain intention informationfrom the motion or speech of a user and determine a response based onthe obtained intention information.

AI+XR

When the AI technology is applied to the XR device 100 c, the XR device100 c may be implemented as a HMD, a HUD mounted in vehicles, a TV, amobile phone, a smartphone, a computer, a wearable device, a homeappliance, a digital signage, a vehicle, a fixed robot, a mobile robot,etc.

The XR device 100 c may analyze three-dimensional point cloud data orimage data obtained from various sensors or external devices, generateposition data and attribute data for three-dimensional points, obtaininformation about a surrounding environment or information about a realobject, perform rendering to on an XR object, and then output the XRobject. For example, the XR device 100 c may output an XR objectincluding information about a recognized object, that is, by matchingthe XR object with the recognized object.

The XR device 100 c may perform the above-described operations based ona learning model configured with at least one ANN. For example, the XRdevice 100 c may recognize the real object from the three-dimensionalpoint cloud data or image data based on the learning model and provideinformation corresponding to the recognized real object. The learningmodel may be directly trained by the XR device 100 c or an externaldevice such as the AI server 200.

The XR device 100 c may operate by directly generating a result based onthe learning model. Alternatively, the XR device 100 c may transmitsensor information to the external device such as the AI server 200 andreceive a result generated based on the sensor information.

AI+Robot+Autonomous Driving

When the AI technology and the autonomous driving technology are appliedto the robot 100 a, the robot 100 a may be implemented as a guide robot,a transport robot, a cleaning robot, a wearable robot, an entertainmentrobot, a pet robot, an unmanned flying robot, etc.

The robot 100 a to which the AI technology and the autonomous drivingtechnology are applied may refer to the robot 100 a with the autonomousdriving function or the robot 100 a interacting with the autonomousdriving vehicle 100 b.

The robot 100 a having the autonomous driving function may becollectively referred to as a device that move along a given movementpath without human control or a device that moves by autonomouslydetermining its movement path.

The robot 100 a having the autonomous driving function and theautonomous driving vehicle 100 b may use a common sensing method todetermine either a travel route or a driving plan. For example, therobot 100 a having the autonomous driving function and the autonomousdriving vehicle 100 b may determine either the travel route or thedriving plan based on information sensed through a LIDAR, a radar, and acamera.

The robot 100 a interacting with the autonomous driving vehicle 100 bmay exist separately from with the autonomous driving vehicle 100 b.That is, the robot 100 a may perform operations associated with theautonomous driving function inside or outside the autonomous drivingvehicle 100 b or interwork with a user on the autonomous driving vehicle100 b.

The robot 100 a interacting with the autonomous driving vehicle 100 bmay control or assist the autonomous driving function of the autonomousdriving vehicle 100 b by obtaining sensor information on behalf of theautonomous driving vehicle 100 b and providing the sensor information tothe autonomous driving vehicle 100 b or by obtaining sensor information,generating environment information or object information, and providingthe information to the autonomous driving vehicle 100 b.

Alternatively, the robot 100 a interacting with the autonomous drivingvehicle 100 b may monitor the user on the autonomous driving vehicle 100b or control the autonomous driving vehicle 100 b through theinteraction with the user. For example, when it is determined that thedriver is in a drowsy state, the robot 100 a may activate the autonomousdriving function of the autonomous driving vehicle 100 b or assist thecontrol of the driving unit of the autonomous driving vehicle 100 b. Thefunction of the autonomous driving vehicle 100 b controlled by the robot100 a may include not only the autonomous driving function but alsofunctions installed in the navigation system or audio system provided inthe autonomous driving vehicle 100 b.

Alternatively, the robot 100 a interacting with the autonomous drivingvehicle 100 b may provide information to the autonomous driving vehicle100 b outside the autonomous driving vehicle 100 b or assist theautonomous driving vehicle 100 b outside the autonomous driving vehicle100 b. For example, the robot 100 a may provide traffic informationincluding signal information such as smart traffic lights to theautonomous driving vehicle 100 b or automatically connect an electriccharger to a charging port by interacting with the autonomous drivingvehicle 100 b like an automatic electric charger installed in anelectric vehicle.

AI+Robot+XR

When the AI technology and the XR technology are applied to the robot100 a, the robot 100 a may be implemented as a guide robot, a transportrobot, a cleaning robot, a wearable robot, an entertainment robot, a petrobot, an unmanned flying robot, a drone, etc.

The robot 100 a to which the XR technology is applied may refer to arobot subjected to control/interaction in an XR image. In this case, therobot 100 a may be separated from the XR device 100 c but interact withthe XR device 100 c.

When the robot 100 a subjected to control/interaction in the XR imageobtains sensor information from sensors including a camera, the robot100 a or XR device 100 c may generate the XR image based on the sensorinformation, and then the XR device 100 c may output the generated XRimage. The robot 100 a may operate based on a control signal inputthrough the XR device 100 c or user interaction.

For example, a user may confirm the XR image corresponding to theperspective of the robot 100 a remotely controlled through an externaldevice such as the XR device 100 c. Then, the user may adjust theautonomous driving path of the robot 100 a or control the operation ormovement of the robot 100 a through interaction therewith or checkinformation about surrounding objects.

AI+Autonomous Driving+XR

When the AI technology and the XR technology is applied to theautonomous driving vehicle 100 b, the autonomous driving vehicle 100 bmay be implemented as a mobile robot, a vehicle, an unmanned flyingvehicle, etc.

The autonomous driving vehicle 100 b to which the XR technology isapplied may refer to an autonomous driving vehicle capable of providingan XR image or an autonomous driving vehicle subjected tocontrol/interaction in an XR image. In particular, the autonomousdriving vehicle 100 b subjected to control/interaction in the XR imagemay be separated from the XR device 100 c but interact with the XRdevice 100 c.

The autonomous driving vehicle 100 b capable of providing the XR imagemay obtain sensor information from sensors including a camera and outputthe generated XR image based on the obtained sensor information. Forexample, the autonomous driving vehicle 100 b may include an HUD foroutputting an XR image, thereby providing a user with an XR objectcorresponding to an object in the screen together with a real object.

When the XR object is displayed on the HUD, at least part of the XRobject may overlap with the real object which the user looks at. On theother hand, when the XR object is displayed on a display provided in theautonomous driving vehicle 100 b, at least part of the XR object mayoverlap with the object in the screen. For example, the autonomousdriving vehicle 100 b may output XR objects corresponding to objectssuch as a lane, another vehicle, a traffic light, a traffic sign, atwo-wheeled vehicle, a pedestrian, a building, etc.

When the autonomous driving vehicle 100 b subjected tocontrol/interaction in the XR image may obtain the sensor informationfrom the sensors including the camera, the autonomous driving vehicle100 b or the XR device 100 c may generate the XR image based on thesensor information, and then the XR device 100 c may output thegenerated XR image. The autonomous driving vehicle 100 b may operatebased on a control signal input through an external device such as theXR device 100 c or user interaction.

The embodiments of the present disclosure described herein below arecombinations of elements and features of the present disclosure. Theelements or features may be considered selective unless otherwisementioned. Each element or feature may be practiced without beingcombined with other elements or features. Further, an embodiment of thepresent disclosure may be constructed by combining parts of the elementsand/or features. Operation orders described in embodiments of thepresent disclosure may be rearranged. Some constructions of any oneembodiment may be included in another embodiment and may be replacedwith corresponding constructions of another embodiment. It will beobvious to those skilled in the art that claims that are not explicitlycited in each other in the appended claims may be presented incombination as an embodiment of the present disclosure or included as anew claim by a subsequent amendment after the application is filed.

In the embodiments of the present disclosure, a description is madecentering on a data transmission and reception relationship among a BS,a relay, and an MS. In some cases, a specific operation described asperformed by the BS may be performed by an upper node of the BS. Namely,it is apparent that, in a network comprised of a plurality of networknodes including a BS, various operations performed for communicationwith an MS may be performed by the BS, or network nodes other than theBS. The term ‘BS’ may be replaced with the term ‘fixed station’, ‘NodeB’, ‘enhanced Node B (eNode B or eNB)’, ‘access point’, etc. The term‘UE’ may be replaced with the term ‘mobile station (MS)’, ‘mobilesubscriber station (MSS)’, ‘mobile terminal’, etc.

The embodiments of the present disclosure may be achieved by variousmeans, for example, hardware, firmware, software, or a combinationthereof. In a hardware configuration, the methods according to theembodiments of the present disclosure may be achieved by one or moreapplication specific integrated circuits (ASICs), digital signalprocessors (DSPs), digital signal processing devices (DSPDs),programmable logic devices (PLDs), field programmable gate arrays(FPGAs), processors, controllers, microcontrollers, microprocessors,etc.

In a firmware or software configuration, the embodiments of the presentdisclosure may be implemented in the form of a module, a procedure, afunction, etc. For example, software code may be stored in a memory unitand executed by a processor. The memory unit is located at the interioror exterior of the processor and may transmit and receive data to andfrom the processor via various known means.

Those skilled in the art will appreciate that the present disclosure maybe carried out in other specific ways than those set forth hereinwithout departing from the spirit and essential characteristics of thepresent disclosure. The above embodiments are therefore to be construedin all aspects as illustrative and not restrictive. The scope of thedisclosure should be determined by the appended claims and their legalequivalents, not by the above description, and all changes coming withinthe meaning and equivalency range of the appended claims are intended tobe embraced therein.

1-10. (canceled)
 11. A method for transmitting a downlink signal by anintegrated access and backhaul (IAB) node in in a wireless communicationsystem, the method comprising: receiving, from a parent node,information about a timing offset; calculating a time difference betweena downlink transmission timing from the parent node and a downlinkreception timing by the TAB node based on a timing advance (TA) for anuplink transmission to the parent node and the timing offset;determining a downlink transmission timing to a child node using thetime difference; and transmitting the downlink signal based on thedownlink transmission timing.
 12. The method of claim 11, wherein thetiming offset is received via a medium access control (MAC) controlelement (CE).
 13. The method of claim 11, wherein the downlinktransmission timing is determined to be preceded from the downlinkreception timing as much as the time difference.
 14. The method of claim11, further comprising receiving information about the TA from theparent node, wherein the downlink transmission timing is not updatedwhen the information about the TA is received.
 15. The method of claim11, wherein the information about the timing offset comprises aninstruction to update the downlink transmission timing.
 16. Anintegrated access and backhaul (IAB) node in in a wireless communicationsystem, the UE comprising: at least one transceiver; at least oneprocessor; and at least one computer memory operably connectable to theat least one processor and storing instructions that, when executed,cause the at least one processor to perform operations comprising:receiving, from a parent node, information about a timing offset;calculating a time difference between a downlink transmission timingfrom the parent node and a downlink reception timing by the TAB nodebased on a timing advance (TA) for an uplink transmission to the parentnode and the timing offset; determining a downlink transmission timingto a child node using the time difference; and transmitting a downlinksignal based on the downlink transmission timing.
 17. The TAB node ofclaim 16, wherein the timing offset is received via a medium accesscontrol (MAC) control element (CE).
 18. The TAB node of claim 16,wherein the downlink transmission timing is determined to be precededfrom the downlink reception timing as much as the time difference. 19.The TAB node of claim 16, wherein the operations further comprisereceiving information about the TA from the parent node, wherein thedownlink transmission timing is not updated when the information aboutthe TA is received.
 20. The TAB node of claim 16, wherein theinformation about the timing offset comprises an instruction to updatethe downlink transmission timing.